HR Technology Conversations: Jan van der Hoop Part II

As more and more technology is created for Human Resources, there is much to be said about its efficacy and the ethical standards by which use is graded. I sat down with HR Exchange Network advisory board member John van der Hoop to discuss the topics. Below is Part II of our conversation.

Transcript:

Mason:

So let’s start with what you asked me to remind you about, which is AI measuring what matters.

Jan:

Yeah. Okay. Let me try to put myself back in my frame of mind from Thursday or Friday when we spoke. Here’s the thing. I think it really does boil down to a thread that I think I laid down last time, which is the importance of measuring what matters, that there’s a risk in HR and I think there’s a risk far beyond HR. In our compulsive desire to capture every snippet of data and ascribe meaning to it, we might completely miss the obvious and the important. Let me give you an example.

In terms of measuring what matters, we’ve worked and collaborated with organizations, both client organizations as well as other vendors who are doing some AI work for those clients. I can think of a number of situations. Here’s a specific example. It was a meat-packing company that was having a tough time. They were working with a third-party temp agency to provide them with folks to work on the line, especially during their peak periods and had employed another organization to do some machine learning using the resumés and whatever other information they could learn about folks, both who had succeeded in this organization and those who had failed.

Over the year, they were able to gather probably something in the order of about 1,500 or 2,000 resumés. They were able to suck all the data into their black box and let it churn. What was interesting is they discovered several things. One was that the resumés themselves weren’t all that distinguishable from one another. As I sat with the client and examined the results, what we discovered, not surprisingly, was a lot of these folks were fairly new immigrants, so new to the country, all came from a specific ethnic group in the neighboring community, and had all had their resumés prepared by the same person at either a community immigrant resettling office or through some other community service.

Here’s where the desire to extract meaningful information starts to fall down if you don’t understand the facts behind the data that you’re parsing. Not surprisingly, their resumés all looked very similar because they’d all been written by the same people and not written by the individuals themselves. So they went through a lot of time and effort to try to analyze, “What are the data points in the documents that we can use as indicators of likely success?” And they didn’t find any, or I think more to the point the information they got was too vague, too broad to be terribly useful.

The one data point that they were able to discover after a little bit more churning was that … I can’t remember exactly what the statistic was, Mason, but it was something along the lines of “If you relied on public transportation to get to work,” which 99% of them did, “and you had more than two transfer points to get to work, then your likelihood of failure in the first 30 days was astronomical.” It wasn’t about the distance. It wasn’t about the radius from work. It wasn’t necessarily even so much about how frequent the buses were. It was about the number of transfers required, which isn’t rocket science.

If somebody in HR had thought to sit down and talk to folks about … Or if their supervisor … Better yet, leave HR out of it because we shouldn’t be inserting ourselves in the relationship between the supervisor and the individual. If the supervisor had had the presence of mind to sit down and chat with folks over coffee during breaks or over lunch to say, “All right. How are things going? How are you finding it?” odds are they would be able to uncover fairly easily the folks who are doing well and those who are feeling a little bit more tenuous just because it’s so inconvenient to get to work. And yet it took this huge machine learning exercise to uncover what should have been obvious if we’d been speaking to our folks.

Mason:

So you think the failure of AI tends to be … It’s almost like Google, where you punch in, “I’m looking for the best Star Trek movie ever,” and it’ll spit out things that … While you may get the answer, you’re also going to get a lot of other stuff that may not be what you’re looking for.

Jan:

Yeah, I think part of it is. If you ask better questions, you’ll get better answers.

Mason:

Right.

Jan:

But I think the other part is we’re so enamored with the technical that we forget to sometimes just sit down and talk to our folks. That touches, I think, on a little bit of what we talked about last week. I mean you can’t create an algorithm for human conversation and human contact and human connection.

Mason:

Do you feel like, with the advent of AI, that most people are forgetting the H part of human resources and that they’re leaning way too much on technology and not finding the correct balance between the two, that maybe AI or data analytics should be complimentary to what you’re supposed to be doing, not become the sole provider of that.

Jan:

Yeah, it’s a great question. I think AI is just the latest opportunity to abdicate responsibility.

Mason:

Makes sense.

Jan:

I’m old enough that I’ve been in the workforce long enough to see some sea changes. When I started my career, granted, it was in an industry that was really focused on its people. I started my business in the hotel, or my career in the hotel industry, which is always lean, always suffers from turnover, long hours, low play. They work really hard. They have to work really hard, or they had to at the time, to attract and keep and develop their folks, so they were just naturally the kind of industry where you had to identify raw talent and figure out how to keep them challenged and engaged.

The way people did that was through the human connection and challenging and stretching and paying attention to how you’re doing and giving you feedback. It was also … When I think about organizations in general in the late ’70s, early ’80s, there tended to be layers of supervisors and managers in place to be the glue in the organization. That just doesn’t exist today. We’ve gone through restructurings and downsizings and all kinds of things. We’ve choked out the middle layer of management.

I’m not saying that’s necessarily a bad thing because there was an awful lot of deadwood, but in so doing what we’ve done is we’ve created now probably three generations of managers who themselves have full inboxes and a long list of tasks and deliverables and budgets and projects and things that they themselves are responsible to do and delivery, that we’ve choked them from the ability to actually pay attention to their folks and connect with them and develop them. This third generation now has never had … It’s probably the fifth generation that’s never had role models to teach them how to do that stuff, and so they respond to the pressures and the demands around them, and so they’re grasping for tools.

They think the tools are going to be what’s missing, the tools will solve everything. Work is not mechanistic. It’s not a production line. People are not widgets that just need oil and a pat on the head occasionally. They require someone to be engaging if they’re going to be engaged. Here’s the piece where it starts to become disconnected for me, back to the theme of needing to measure what matters most. We rush headlong around parsing resumés and looking for correlations to performance and turnover and all those other things.

90% of the time when it doesn’t work out, 90% of the time when someone fails in their job, it has nothing to do with what they know. And it rarely really has … If they’ve been there any length of time, it rarely has anything to do with how many transfers they had to take to get to work. It has to do with their fundamental compatibility with their manager, with the work they’re being asked to do, with the people that they spend their day with, and with the culture and the environment in which they’re working at that organization.

That’s where measuring the softer aspect, looking for indicators of fit of the individual becomes really critical, not necessarily as a complete replacement for measuring all the other stuff, but certainly as the first filter. If it’s not the right person who’s applying for the work, the fact that they can just take a direct bus to work without any transfers isn’t going to make them last any longer.

Mason:

Right. You kind of mentioned something that I want to explore just a little bit more because I was talking with [inaudible 00:11:02] from P.F. Chang’s and we were talking a little bit about the generational mix of the workforce now. He said something similar to what you said in terms of like you’ve got Baby Boomers who are in their 30s and 40s … Or that’s not correct.

Jan:

No, we’re a little older than that.

Mason:

Yeah. Sorry. See, this is what happens. I need another cup of coffee. Jeez Louise. You’ve got managers that are in their 30s and 40s that are learning to be managers, but then you have younger managers that are in their 20s that you have to actually teach how to lead people and have to teach them …

Jan:

Yeah.

Mason:

… how to function.

Jan:

Well, how many of us came out of the womb ready to do it?

Mason:

I know. Right? It just becomes a process of … That’s part of the challenge in terms of … I was talking to him more about the way they’ve changed the culture at P.F. Chang’s and what they’re doing to change it more to a human-centric kind of culture, and that was what he was talking about in terms of the workforce. You have these individuals that are in here that …

You’ve got some people that just want to come to work to get a paycheck, and then you’ve got some people that are there to actually be a part of something, and then you’ve got people that are in the middle that have no idea what the heck they’re doing. It’s trying to get everybody on the same page. From his perspective, part of the answer is technology, but it’s not the sole answer.

Jan:

No.

Mason:

It’s through learning. It’s through coaching. It’s through remembering, really, what their core values are and what they’re trying to be.

Jan:

Yeah, and what stage of life they’re at and what’s going on for them in the 16 hours a day they’re not at work and “What demands are being placed on them? What are their dreams? What are their hopes? What are their frustrations?” I mean it’s all basic stuff. And you’re right. I think for that group in the middle that are trying to figure it out, for them their manager needs to be the giver of meaning, needs to be the one who answers their questions so that they can figure out for themselves what’s right, whether this is right, whether I want to grow here or whether I just want to be a steady heady. There’s no shame in that. There’s a lot of folks who come to work every day, and they’re absolutely excellent at what they do, and they love it, and they’re engaged, and they don’t want more responsibility than that. That should be celebrated, and we tend to look down our nose at those folks very often.

Mason:

Because we don’t tend to think about people that … They have it in them to be good employees, but they don’t have to be ambitious to be good employees.

Jan:

No. Yeah, and we lose sight of the nobility of just steady, solid, unremarkable performance.

Mason:

Let’s kind of move that … Since we’re talking about that, let’s move it to recognition and rewards, if you will.

Jan:

Okay.

Mason:

Some folks are starting to redesign their rewards and recognition from a technological standpoint, and then some are doing it from like a peer-to-peer. What do you think in terms of … If somebody is taking on this process, what do you think has to be the mission of that type of thing?

Jan:

Really?

Mason:

You hate me. You hate me. I know it. It’s …

Jan:

No, you just tug at my dark side. Here’s the thing, and it’s more of the same, Mason. If done well, a recognition … Any approach to recognition, however high tech or low tech it is, is going to be meaningful if it’s sincere, if it catches people in the act of doing things excellently, and if it creates an opportunity for them to learn and appreciate and internalize what it is they do really well that makes a difference for the organization.

Mason:

That’s exactly what he said when I was talking to P.F. Chang’s.

Jan:

Yeah. So it doesn’t matter. It could be a fraction of a second eye contact from the supervisor and a nod saying, “Attaboy.” That can buoy somebody’s spirit considerably. Or it could be an online gamified thing where I earn points and if I earn extra points I can get a free tank of gas in my car or whatever it is. I think the answer to the mechanism really depends on the culture of the environment. It depends on the hard-wiring of the people who are there.

And it requires the manager to be really aware of what it is that drives positive behavior in each of their folks. I think any system, however elaborate, risks appealing to and motivating a segment of the workforce and alienating another. When it comes to motivation, when it comes to rewards and recognition, one size does not fit all. If done right, it’s an intimate thing. It has to be heart to heart. It can’t be company logo to pocket book or company logo to gas tank or what have you. That becomes a meaningless game.

Mason:

I think in general, not to simplify the message, but to kind of get it down to a bit of a bite size, the headline here is “Technology is great when used in the appropriate fashion and used to fit and to compliment your workforce, but it shouldn’t be used to necessarily take over ownership or management of the workforce.”

Jan:

Right. Yeah, well put. I mean it should enable. It should facilitate. It should allow me to have bigger impact with less effort as the manager, but I can’t use it as my surrogate. The technology is not going to touch somebody else’s heart and have them do more of the right thing.

Mason:

Right.

Jan:

I have to be present in that in order for it to happen.

Mason:

Right. It can be a cold touch to let technology stand in the place of a human being.

Jan:

Yeah, and it risks backfiring.

Mason:

Let’s talk a little bit about technology and employee engagement in terms of how best an organization might use that. I know it’s dependent upon the organization itself: what they’re doing, what their clients require of them and so on and so forth. How do you think the appropriate way to go about employee engagement through technology, whether it’s … For instance, would it be potentially through like a social type thing? Or is it through an internal workforce management type of technology, whether it’s sharing schedules? Or how does that engagement impact the workforce itself and the culture at the company?

Jan:

All right. I think you’re going to hear a recurring theme from me. Here’s the thing. I was really interested when Gallup first started researching engagement back in the mid ’90s, and all of the research that’s gone into it, into this bonanza of engagement that the consulting companies have all dived on continues to underscore the same thing. You can’t engage the unengageable, and so unless … If you take the value chain and work it backwards, organizations are looking for improvement in performance, so increased productivity, reduced turnover, happier customers, better financial performance.

Those are all outcomes. In Gallup’s words, they’re trailing indicators. They’re the results of other things either done well or done poorly. As you work backwards up the chain … Gallup’s initial research back in the ’90s was really centered around “What are the leading indicators of those outcomes?” They tested everything they could find, everything from investments in research development to the amount of training people were given to other product development initiatives. They tripped on this thing that they … They coined the phrase employee engagement.

Employee engagement was the statistically most valid predictor of hard business outcomes. And they also knew that engagement wasn’t the leading indicator, that conditions have to be present in order for engagement to happen. So I think if you peel it back one further step, that’s where … I mean in Gallup’s parlance, that’s where they came up with the Q12, which is a set of 12 questions that they identified as the most reliable indicators of whether engagement exists in a work group. They’re really all about relationships.

We tend to use the term fit, but it’s the quality of fit or the quality of relationships really across the same four buckets that we talked about earlier on, so fit with manager because that’s the single most important relationship for all of us at work. Second is fit or compatibility with the work. “Is the work that I’m asked to do eight hours a day playing to my natural strengths more than it’s asking me to draw from things that I’m never really going to be great at? And can I find a way to give a damn?” “Can I find a way to care about the quality of my output?”

The third aspect of fit or relationship is with my peers or the people I’m spending my day with, so that could be coworkers if I’m in a position that’s internally facing or it could be fit with my customers, but essentially it’s, “Do I feel an affinity with the people that I spend eight hours a day with? Do I genuinely like and care about them? And do I feel liked and respected and cared about them in return?” Then finally it’s fit with the organizational culture and values and “Is this a place I feel proud to tell my friends I come to work?” Those are the four critical aspects of fit.

And what all of the research continues to show is, unless you’ve got the right people focused on the right things, fit is going to be really hard to achieve. Even if you bring in people who are a great fit for the environment and across all four aspects of fit and you set them up in the right situation … The metaphor would be, you take this plant that you brought at the garden center, and you’re putting it in exactly the right soil and light conditions. It still requires a manager who’s engaging to be the catalyst that transforms this engageable state into engagement. Does that make any sense to you?

Mason:

It does make sense, yeah.

Jan:

So you can have all the necessary tools in place to help you bring in people who are a great fit, and they have all the right experience and knowledge, but if their manager is a dick or the manager doesn’t have the tools or the time to focus on actually converting this engageable state into results, it ain’t going to happen.

Mason:

That’s like the technical explanation of something I’ve always said from a journalistic standpoint. One of the things that I always struggled with is one thing that I expect out of my managers and out of my leaders in the workplace is that they had to inspire me to continue doing what I’m doing because the work is hard. There are hard things that you do in terms of, as a journalist, covering a child being killed or a mass shooting or something to that effect. A leader’s responsibility in that perspective is to continue to inspire you to move on and to continue doing what you’re doing and to remind you that these things matter. But when they fall into this category of ineptness at that inspiration or when they themselves don’t give a damn, it makes it-

Jan:

Or indifference, right.

Mason:

It makes it more difficult for those of us that do to continue to do so and to continue to be inspired to do so.

Jan:

Right. Back to your question about technology, you can use technology to help you improve your odds dramatically to make sure that you’re putting the right people in the right situation. You can use technology to improve the operating efficiency of the team dramatically. Those are all tools that will help improve your odds and facilitate more graceful outcomes with less energy and friction required. All of those things require the human element in order to have context and utility.

Mason:

Right, to make it function.

Jan:

That’s right.

Mason:

I think that’s all I’ll pester you with because I know you’ve got to get back to your normal gig, if you will, and take off your [crosstalk 00:26:52]

Jan:

I’ve got to calm down now.

Mason:

It’s like, “I’m never going to talk to him again. Oh my God, I feel like I have to go have a drink after talking to him,” and now you know how people in my life feel when they talk to me.

Jan:

Yeah. Here’s the thing. I feel like this has taken you off the intended arc for the article, but it’s stuff that I really believe firmly.

Mason:

I will be honest with you, it has, but not in a way that is negative because, as someone who is coming into this world basically as an infant, if you will, because I’ve not had as much impact in the HR world in my past life as a journalist, if you will, as I do now, I have a lot to learn. The most significant thing is that I’ve learned a lot here, and it’s helped confirm some of my already held beliefs about HR.

It’s given me something to think about in some other places, and I think that I have to translate that out to the people who look to [HR-EN 00:28:07] for sources of information and thought leadership and guidance, which it’s very, very important for me to do that. And I’m not smart enough to do that on my own. That’s why I need people like you to help me do that. So I won’t pick your brain anymore about the topics right now, but I would like to continue the conversation just as we go on, just …

Jan:

Yeah, absolutely.

Mason:

… occasionally to pick your brain. We have an advisory board, and basically those folks are people that I can kind of go to and say, “Hey, I’m doing some research on this. I know you have some expertise here. Can I pick your brain?” or “I’m having trouble understanding something specifically. Can you help dumb it down for Mason? I need an idiot’s guide here.”

Jan:

Or translate it to common sense, which is probably more likely.

Mason:

Right. Right. Translate it for Mason. And I definitely want to lean on you in that fact. Would you … Of course, I have to kind of go over this on my side with my management. Would you maybe consider being an advisory board member if that were something available to you?

Jan:

Sure, any way I can support, Mason. I mean I’m probably … In a lot of respects, I’m a bit of a contrarian in that my views tend to be a little bit sideways from the conventional way of thinking about things, and sometimes that can be helpful, and sometimes it can be a bad thing, so I’m happy to insert my views where you think it would be helpful.

Mason:

Right. I mean just in myself, just in our conversations … because I’ve spoken to a lot of people just in my few months here, 30 or 40 people. And I’ve learned a lot from them, but I’ve certainly a lot from the two conversations that we’ve had, and I think that what you have to offer is valuable in terms of … If there’s one thing I’ve felt and I’ve learned or that I see is that technology is like a really shiny toy and people are like, “Oh, look what I … Look at this. Look at this. This is great. Oh, look at the kitty,” and their attention goes somewhere else, and they forget, “Wait a minute. I have something important here that I need to focus on, and that’s the people.”

I think what you’ve offered even to me is that it’s like, “Reel it back just a little bit. Stop for just a second. Breathe, look, understand, but don’t jump head in,” and I think that’s an important piece to this. I think that people, generally speaking, have that in their head, but it almost becomes an afterthought rather than a conscious thought. I think it’s important that we reel that in from time to time, and so contrary to what you think, I think it’s very valuable from that perspective, to remind people of what they’re doing.

Jan:

Okay. Cool. Well, happy to be [inaudible 00:31:34] second thought.

Mason:

Cool. Awesome. Well, let me get off here and let you get back to your normal, day-to-day stuff. I’ll start putting this together. I’m not sure how I’m going to form it just yet. I want to go back to listen to our first conversation and obviously listen to this one again and start mulling something together. It may be a composite piece of a couple of different things.

I think the one most significant thing is the ethics of AI because I’ve not seen a lot in terms of research, and you’ve very succinctly explained kind of the ethics of it. I think that that’s important. Again, it’s a shiny new toy, but people are forgetting its implications, and I think that’s important. I mean you’ve got Elon Musk out there who’s like, “Eh, chill out.”

Jan:

Yeah. He’s saying, “Back away. Be really careful.”

Mason:

Well, and the backlash to that … There was a backlash in that [inaudible 00:32:36] It’s like, “Why are you saying this? You would be the person that should be pushing something like this.” I think any time somebody that’s in that world that gives pause, I think that’s significant and I think people need to pay attention because they’re not doing it to be butt holes. They’re doing it because there’s a legitimate reason to do it.

Jan:

Yeah. It’s not something we can afford to lose control over. It’s not a lot different than monkeying around with DNA. Christ knows what you’re going to create. And whose hands is it going to fall into?

Mason:

Right, and I think a large part of that is we simply just do not understand all of it yet because this is-

Jan:

No, we don’t.

Mason:

We’ve talked about AI for years in terms of science fiction, but now it’s become a reality, and we have to put it … Let me use this to describe it. Just within the last few weeks … I don’t know if you caught this article or not, but just within the last few weeks the United … I think it’s United Arab Emirates. I could be wrong about that. Saudi Arabia I think it was. It was a PR stunt though, but they basically gave Sophia, which is an AI robot, honorary citizenship.

Now, it was a PR stunt to draw more attention to their technology conference that was happening there, but the PR stunt in itself had a lot of reverberations in a lot of different ways. One, just humanly speaking, the country just gave women the right to drive just within the last month, and here they are pulling a stunt giving AI honorary citizenship. While it being a stunt and they didn’t actually do it, the implications of it are hefty.

Jan:

Yeah. While half the population isn’t a full citizen.

Mason:

Right, and so the human implications of that are catastrophic, but the other thing is, while being a stunt, it really started to conversation of “How does AI and how do robots actually fit into our society?” and “How are we going to apply that down the road?” I think people with big noggins, myself not included in that group, are a little bit more engaged in that conversation, but the reality is that I think we all can agree that AI and robotics are going to … maybe not in my lifetime, but eventually become a huge part of the day-to-day lives of just regular individuals. Just look at Siri and Alexa for crying out loud.

Jan:

Well, Gene Roddenberry was the first to go up that path 30 years ago with data and “Is he human? Does he have feelings? Is he sentient?” That’s been an existential question for decades.

Mason:

You are talking to the hugest Star Trek fan ever, so I totally agree. I’m not kidding you man. I have posters. I have all the seasons on DVD. I’m paying $11 a month to watch Discovery on CBS All Access. It’s bad. But you’re right. I mean what was an existential question is now becoming a valid question.

Jan:

Yeah. It’s not that far out there.

Mason:

It’s not, and I don’t think people realize that. Again, the big-brained people, they’re starting to get it, but everyday [inaudible 00:36:22] like myself … Well, maybe not like myself because I do think about it some, but your average person, they just don’t realize it.

Jan:

It’s not far over the horizon to think that we may be working for robotic bosses. Back to the ethical question, to me that could play one of two very different ways. In the Pollyanna world, it could be your robo-boss coming to you and saying, “Mason, I’ve noticed that you’re flushed, your stress levels seem high, your productivity level has gone down a little bit over the last two days. Is there something going on that I can support you with?” Or that same robo-boss could just as easily say to you, “Mason, I’ve noticed that your productivity has gone down and you’ve been taking two minutes longer at breaks. You’re going to be docked for the time. Keep it up and you’re not going to have a job here.”

Mason:

Right.

Jan:

And so it goes back to that first fundamental conversation we had about ethics. Both are equally realistic outcomes, soon to be realities, I would say in some workplaces in as little as five years or even less.

Mason:

Wow, you think much sooner than maybe I had even begun to think.

Jan:

If we can program the AI to notice differences in performances, to notice differences in aspect, in attitude, to notice differences in motivation that managers are blind to either because they don’t have the eyes to see it or because they’re too damn busy to see it, then why wouldn’t you use a robot to do it because they’re patient and they notice everything?

Mason:

Yeah, that’s legitimate. I think, generally speaking, from a basic standpoint, that’s what technology is supposed to do, in terms of taking on some responsibilities so managers can be more attentive to the human side of their jobs, but in some instances there’s some concern that we could see even those pieces being forfeited to technology.

Jan:

Yeah, and there’s the risk. So there you go. I think you’re picking at the right things. I’m excited to see what you create with all of these inputs from these interviews.

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